Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations150000
Missing cells1123500
Missing cells (%)35.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory100.7 MiB
Average record size in memory704.0 B

Variable types

DateTime2
Text4
Categorical9
Numeric6

Alerts

Cancelled Rides by Customer has constant value "1.0" Constant
Cancelled Rides by Driver has constant value "1.0" Constant
Incomplete Rides has constant value "1.0" Constant
Booking Status is highly overall correlated with Customer Rating and 4 other fieldsHigh correlation
Customer Rating is highly overall correlated with Booking StatusHigh correlation
Driver Cancellation Reason is highly overall correlated with Booking StatusHigh correlation
Driver Ratings is highly overall correlated with Booking StatusHigh correlation
Incomplete Rides Reason is highly overall correlated with Booking StatusHigh correlation
Reason for cancelling by Customer is highly overall correlated with Booking StatusHigh correlation
Avg VTAT has 10500 (7.0%) missing values Missing
Avg CTAT has 48000 (32.0%) missing values Missing
Cancelled Rides by Customer has 139500 (93.0%) missing values Missing
Reason for cancelling by Customer has 139500 (93.0%) missing values Missing
Cancelled Rides by Driver has 123000 (82.0%) missing values Missing
Driver Cancellation Reason has 123000 (82.0%) missing values Missing
Incomplete Rides has 141000 (94.0%) missing values Missing
Incomplete Rides Reason has 141000 (94.0%) missing values Missing
Booking Value has 48000 (32.0%) missing values Missing
Ride Distance has 48000 (32.0%) missing values Missing
Driver Ratings has 57000 (38.0%) missing values Missing
Customer Rating has 57000 (38.0%) missing values Missing
Payment Method has 48000 (32.0%) missing values Missing

Reproduction

Analysis started2025-08-30 11:27:45.661032
Analysis finished2025-08-30 11:28:04.408630
Duration18.75 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Date
Date

Distinct365
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Minimum2024-01-01 00:00:00
Maximum2024-12-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-30T14:28:04.490888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:04.587886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Time
Date

Distinct62910
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Minimum2025-08-30 00:00:00
Maximum2025-08-30 23:59:59
Invalid dates0
Invalid dates (%)0.0%
2025-08-30T14:28:04.681886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:04.776886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct148767
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size8.7 MiB
2025-08-30T14:28:05.029262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1800000
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique147543 ?
Unique (%)98.4%

Sample

1st row"CNR5884300"
2nd row"CNR1326809"
3rd row"CNR8494506"
4th row"CNR8906825"
5th row"CNR1950162"
ValueCountFrequency (%)
cnr6337479 3
 
< 0.1%
cnr3648267 3
 
< 0.1%
cnr9603232 3
 
< 0.1%
cnr5292943 3
 
< 0.1%
cnr7585544 3
 
< 0.1%
cnr7199036 3
 
< 0.1%
cnr7642097 3
 
< 0.1%
cnr2726142 3
 
< 0.1%
cnr7908610 3
 
< 0.1%
cnr2869280 2
 
< 0.1%
Other values (148757) 149971
> 99.9%
2025-08-30T14:28:05.310471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
" 300000
16.7%
C 150000
 
8.3%
N 150000
 
8.3%
R 150000
 
8.3%
9 107075
 
5.9%
5 106822
 
5.9%
4 106770
 
5.9%
7 106760
 
5.9%
6 106752
 
5.9%
8 106627
 
5.9%
Other values (4) 409194
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1800000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
" 300000
16.7%
C 150000
 
8.3%
N 150000
 
8.3%
R 150000
 
8.3%
9 107075
 
5.9%
5 106822
 
5.9%
4 106770
 
5.9%
7 106760
 
5.9%
6 106752
 
5.9%
8 106627
 
5.9%
Other values (4) 409194
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1800000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
" 300000
16.7%
C 150000
 
8.3%
N 150000
 
8.3%
R 150000
 
8.3%
9 107075
 
5.9%
5 106822
 
5.9%
4 106770
 
5.9%
7 106760
 
5.9%
6 106752
 
5.9%
8 106627
 
5.9%
Other values (4) 409194
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1800000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
" 300000
16.7%
C 150000
 
8.3%
N 150000
 
8.3%
R 150000
 
8.3%
9 107075
 
5.9%
5 106822
 
5.9%
4 106770
 
5.9%
7 106760
 
5.9%
6 106752
 
5.9%
8 106627
 
5.9%
Other values (4) 409194
22.7%

Booking Status
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.7 MiB
Completed
93000 
Cancelled by Driver
27000 
No Driver Found
10500 
Cancelled by Customer
10500 
Incomplete
 
9000

Length

Max length21
Median length9
Mean length12.12
Min length9

Characters and Unicode

Total characters1818000
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Driver Found
2nd rowIncomplete
3rd rowCompleted
4th rowCompleted
5th rowCompleted

Common Values

ValueCountFrequency (%)
Completed 93000
62.0%
Cancelled by Driver 27000
 
18.0%
No Driver Found 10500
 
7.0%
Cancelled by Customer 10500
 
7.0%
Incomplete 9000
 
6.0%

Length

2025-08-30T14:28:05.375515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T14:28:05.432516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
completed 93000
37.8%
cancelled 37500
15.2%
by 37500
15.2%
driver 37500
15.2%
no 10500
 
4.3%
found 10500
 
4.3%
customer 10500
 
4.3%
incomplete 9000
 
3.7%

Most occurring characters

ValueCountFrequency (%)
e 327000
18.0%
l 177000
9.7%
C 141000
 
7.8%
d 141000
 
7.8%
o 133500
 
7.3%
t 112500
 
6.2%
m 112500
 
6.2%
p 102000
 
5.6%
96000
 
5.3%
r 85500
 
4.7%
Other values (13) 390000
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1818000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 327000
18.0%
l 177000
9.7%
C 141000
 
7.8%
d 141000
 
7.8%
o 133500
 
7.3%
t 112500
 
6.2%
m 112500
 
6.2%
p 102000
 
5.6%
96000
 
5.3%
r 85500
 
4.7%
Other values (13) 390000
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1818000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 327000
18.0%
l 177000
9.7%
C 141000
 
7.8%
d 141000
 
7.8%
o 133500
 
7.3%
t 112500
 
6.2%
m 112500
 
6.2%
p 102000
 
5.6%
96000
 
5.3%
r 85500
 
4.7%
Other values (13) 390000
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1818000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 327000
18.0%
l 177000
9.7%
C 141000
 
7.8%
d 141000
 
7.8%
o 133500
 
7.3%
t 112500
 
6.2%
m 112500
 
6.2%
p 102000
 
5.6%
96000
 
5.3%
r 85500
 
4.7%
Other values (13) 390000
21.5%
Distinct148788
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size8.7 MiB
2025-08-30T14:28:05.629206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1800000
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique147582 ?
Unique (%)98.4%

Sample

1st row"CID1982111"
2nd row"CID4604802"
3rd row"CID9202816"
4th row"CID2610914"
5th row"CID9933542"
ValueCountFrequency (%)
cid8727691 3
 
< 0.1%
cid4523979 3
 
< 0.1%
cid6715450 3
 
< 0.1%
cid7828101 3
 
< 0.1%
cid6468528 3
 
< 0.1%
cid5481002 3
 
< 0.1%
cid4977850 2
 
< 0.1%
cid7088244 2
 
< 0.1%
cid7294800 2
 
< 0.1%
cid2877369 2
 
< 0.1%
Other values (148778) 149974
> 99.9%
2025-08-30T14:28:05.884224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
" 300000
16.7%
C 150000
 
8.3%
I 150000
 
8.3%
D 150000
 
8.3%
4 107135
 
6.0%
1 107018
 
5.9%
6 107016
 
5.9%
9 106654
 
5.9%
7 106632
 
5.9%
5 106484
 
5.9%
Other values (4) 409061
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1800000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
" 300000
16.7%
C 150000
 
8.3%
I 150000
 
8.3%
D 150000
 
8.3%
4 107135
 
6.0%
1 107018
 
5.9%
6 107016
 
5.9%
9 106654
 
5.9%
7 106632
 
5.9%
5 106484
 
5.9%
Other values (4) 409061
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1800000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
" 300000
16.7%
C 150000
 
8.3%
I 150000
 
8.3%
D 150000
 
8.3%
4 107135
 
6.0%
1 107018
 
5.9%
6 107016
 
5.9%
9 106654
 
5.9%
7 106632
 
5.9%
5 106484
 
5.9%
Other values (4) 409061
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1800000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
" 300000
16.7%
C 150000
 
8.3%
I 150000
 
8.3%
D 150000
 
8.3%
4 107135
 
6.0%
1 107018
 
5.9%
6 107016
 
5.9%
9 106654
 
5.9%
7 106632
 
5.9%
5 106484
 
5.9%
Other values (4) 409061
22.7%

Vehicle Type
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
Auto
37419 
Go Mini
29806 
Go Sedan
27141 
Bike
22517 
Premier Sedan
18111 
Other values (2)
15006 

Length

Max length13
Median length8
Mean length6.5659
Min length4

Characters and Unicode

Total characters984885
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st roweBike
2nd rowGo Sedan
3rd rowAuto
4th rowPremier Sedan
5th rowBike

Common Values

ValueCountFrequency (%)
Auto 37419
24.9%
Go Mini 29806
19.9%
Go Sedan 27141
18.1%
Bike 22517
15.0%
Premier Sedan 18111
12.1%
eBike 10557
 
7.0%
Uber XL 4449
 
3.0%

Length

2025-08-30T14:28:05.948949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T14:28:06.007290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
go 56947
24.8%
sedan 45252
19.7%
auto 37419
16.3%
mini 29806
13.0%
bike 22517
 
9.8%
premier 18111
 
7.9%
ebike 10557
 
4.6%
uber 4449
 
1.9%
xl 4449
 
1.9%

Most occurring characters

ValueCountFrequency (%)
e 129554
13.2%
i 110797
11.2%
o 94366
 
9.6%
79507
 
8.1%
n 75058
 
7.6%
G 56947
 
5.8%
a 45252
 
4.6%
d 45252
 
4.6%
S 45252
 
4.6%
r 40671
 
4.1%
Other values (12) 262229
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 984885
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 129554
13.2%
i 110797
11.2%
o 94366
 
9.6%
79507
 
8.1%
n 75058
 
7.6%
G 56947
 
5.8%
a 45252
 
4.6%
d 45252
 
4.6%
S 45252
 
4.6%
r 40671
 
4.1%
Other values (12) 262229
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 984885
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 129554
13.2%
i 110797
11.2%
o 94366
 
9.6%
79507
 
8.1%
n 75058
 
7.6%
G 56947
 
5.8%
a 45252
 
4.6%
d 45252
 
4.6%
S 45252
 
4.6%
r 40671
 
4.1%
Other values (12) 262229
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 984885
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 129554
13.2%
i 110797
11.2%
o 94366
 
9.6%
79507
 
8.1%
n 75058
 
7.6%
G 56947
 
5.8%
a 45252
 
4.6%
d 45252
 
4.6%
S 45252
 
4.6%
r 40671
 
4.1%
Other values (12) 262229
26.6%
Distinct176
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.7 MiB
2025-08-30T14:28:06.174512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length19
Mean length11.505627
Min length3

Characters and Unicode

Total characters1725844
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPalam Vihar
2nd rowShastri Nagar
3rd rowKhandsa
4th rowCentral Secretariat
5th rowGhitorni Village
ValueCountFrequency (%)
nagar 14525
 
5.2%
vihar 8410
 
3.0%
chowk 6860
 
2.5%
park 5246
 
1.9%
sector 5031
 
1.8%
gurgaon 5016
 
1.8%
road 4244
 
1.5%
noida 4209
 
1.5%
city 3460
 
1.2%
garden 3408
 
1.2%
Other values (214) 217828
78.3%
2025-08-30T14:28:06.402689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 277598
16.1%
r 128582
 
7.5%
128237
 
7.4%
i 109699
 
6.4%
h 89268
 
5.2%
n 74783
 
4.3%
e 72093
 
4.2%
o 68791
 
4.0%
t 56145
 
3.3%
u 53591
 
3.1%
Other values (48) 667057
38.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1725844
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 277598
16.1%
r 128582
 
7.5%
128237
 
7.4%
i 109699
 
6.4%
h 89268
 
5.2%
n 74783
 
4.3%
e 72093
 
4.2%
o 68791
 
4.0%
t 56145
 
3.3%
u 53591
 
3.1%
Other values (48) 667057
38.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1725844
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 277598
16.1%
r 128582
 
7.5%
128237
 
7.4%
i 109699
 
6.4%
h 89268
 
5.2%
n 74783
 
4.3%
e 72093
 
4.2%
o 68791
 
4.0%
t 56145
 
3.3%
u 53591
 
3.1%
Other values (48) 667057
38.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1725844
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 277598
16.1%
r 128582
 
7.5%
128237
 
7.4%
i 109699
 
6.4%
h 89268
 
5.2%
n 74783
 
4.3%
e 72093
 
4.2%
o 68791
 
4.0%
t 56145
 
3.3%
u 53591
 
3.1%
Other values (48) 667057
38.7%
Distinct176
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.7 MiB
2025-08-30T14:28:06.548950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length20
Mean length11.511813
Min length3

Characters and Unicode

Total characters1726772
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJhilmil
2nd rowGurgaon Sector 56
3rd rowMalviya Nagar
4th rowInderlok
5th rowKhan Market
ValueCountFrequency (%)
nagar 14533
 
5.2%
vihar 8538
 
3.1%
chowk 6656
 
2.4%
sector 5148
 
1.8%
gurgaon 5084
 
1.8%
park 5002
 
1.8%
noida 4315
 
1.5%
road 4130
 
1.5%
gate 3481
 
1.2%
place 3458
 
1.2%
Other values (214) 218245
78.3%
2025-08-30T14:28:06.781691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 277071
16.0%
r 128702
 
7.5%
128590
 
7.4%
i 109858
 
6.4%
h 89698
 
5.2%
n 74820
 
4.3%
e 72505
 
4.2%
o 69018
 
4.0%
t 56302
 
3.3%
u 53474
 
3.1%
Other values (48) 666734
38.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1726772
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 277071
16.0%
r 128702
 
7.5%
128590
 
7.4%
i 109858
 
6.4%
h 89698
 
5.2%
n 74820
 
4.3%
e 72505
 
4.2%
o 69018
 
4.0%
t 56302
 
3.3%
u 53474
 
3.1%
Other values (48) 666734
38.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1726772
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 277071
16.0%
r 128702
 
7.5%
128590
 
7.4%
i 109858
 
6.4%
h 89698
 
5.2%
n 74820
 
4.3%
e 72505
 
4.2%
o 69018
 
4.0%
t 56302
 
3.3%
u 53474
 
3.1%
Other values (48) 666734
38.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1726772
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 277071
16.0%
r 128702
 
7.5%
128590
 
7.4%
i 109858
 
6.4%
h 89698
 
5.2%
n 74820
 
4.3%
e 72505
 
4.2%
o 69018
 
4.0%
t 56302
 
3.3%
u 53474
 
3.1%
Other values (48) 666734
38.6%

Avg VTAT
Real number (ℝ)

Missing 

Distinct181
Distinct (%)0.1%
Missing10500
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean8.456352
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-08-30T14:28:06.848958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.9
Q15.3
median8.3
Q311.3
95-th percentile14.6
Maximum20
Range18
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.7735638
Coefficient of variation (CV)0.44624016
Kurtosis-0.59788646
Mean8.456352
Median Absolute Deviation (MAD)3
Skewness0.30656478
Sum1179661.1
Variance14.239784
MonotonicityNot monotonic
2025-08-30T14:28:06.943483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.4 1259
 
0.8%
6 1249
 
0.8%
5.8 1248
 
0.8%
9.5 1246
 
0.8%
6.5 1239
 
0.8%
6.2 1237
 
0.8%
5.1 1235
 
0.8%
7.7 1230
 
0.8%
6.7 1228
 
0.8%
8.9 1224
 
0.8%
Other values (171) 127105
84.7%
(Missing) 10500
 
7.0%
ValueCountFrequency (%)
2 411
0.3%
2.1 838
0.6%
2.2 787
0.5%
2.3 791
0.5%
2.4 810
0.5%
2.5 819
0.5%
2.6 812
0.5%
2.7 787
0.5%
2.8 814
0.5%
2.9 824
0.5%
ValueCountFrequency (%)
20 38
< 0.1%
19.9 84
0.1%
19.8 78
0.1%
19.7 68
< 0.1%
19.6 78
0.1%
19.5 57
< 0.1%
19.4 92
0.1%
19.3 61
< 0.1%
19.2 75
0.1%
19.1 59
< 0.1%

Avg CTAT
Real number (ℝ)

Missing 

Distinct351
Distinct (%)0.3%
Missing48000
Missing (%)32.0%
Infinite0
Infinite (%)0.0%
Mean29.149636
Minimum10
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-08-30T14:28:07.032349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile15.8
Q121.6
median28.8
Q336.8
95-th percentile43.4
Maximum45
Range35
Interquartile range (IQR)15.2

Descriptive statistics

Standard deviation8.9025772
Coefficient of variation (CV)0.30540955
Kurtosis-1.1235657
Mean29.149636
Median Absolute Deviation (MAD)7.6
Skewness0.045899577
Sum2973262.9
Variance79.255882
MonotonicityNot monotonic
2025-08-30T14:28:07.117759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.8 401
 
0.3%
25.9 389
 
0.3%
28.1 388
 
0.3%
20.5 386
 
0.3%
25.3 383
 
0.3%
17.2 382
 
0.3%
23.5 381
 
0.3%
18.5 380
 
0.3%
27.7 380
 
0.3%
20 380
 
0.3%
Other values (341) 98150
65.4%
(Missing) 48000
32.0%
ValueCountFrequency (%)
10 23
< 0.1%
10.1 48
< 0.1%
10.2 44
< 0.1%
10.3 43
< 0.1%
10.4 52
< 0.1%
10.5 48
< 0.1%
10.6 47
< 0.1%
10.7 48
< 0.1%
10.8 43
< 0.1%
10.9 43
< 0.1%
ValueCountFrequency (%)
45 144
0.1%
44.9 318
0.2%
44.8 315
0.2%
44.7 333
0.2%
44.6 306
0.2%
44.5 296
0.2%
44.4 320
0.2%
44.3 259
0.2%
44.2 304
0.2%
44.1 317
0.2%

Cancelled Rides by Customer
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing139500
Missing (%)93.0%
Memory size8.0 MiB
1.0
10500 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters31500
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 10500
 
7.0%
(Missing) 139500
93.0%

Length

2025-08-30T14:28:07.195506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T14:28:07.232589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 10500
100.0%

Most occurring characters

ValueCountFrequency (%)
1 10500
33.3%
. 10500
33.3%
0 10500
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 10500
33.3%
. 10500
33.3%
0 10500
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 10500
33.3%
. 10500
33.3%
0 10500
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 10500
33.3%
. 10500
33.3%
0 10500
33.3%

Reason for cancelling by Customer
Categorical

High correlation  Missing 

Distinct5
Distinct (%)< 0.1%
Missing139500
Missing (%)93.0%
Memory size8.2 MiB
Wrong Address
2362 
Change of plans
2353 
Driver is not moving towards pickup location
2335 
Driver asked to cancel
2295 
AC is not working
1155 

Length

Max length44
Median length17
Mean length22.749143
Min length13

Characters and Unicode

Total characters238866
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDriver is not moving towards pickup location
2nd rowDriver is not moving towards pickup location
3rd rowDriver asked to cancel
4th rowDriver is not moving towards pickup location
5th rowDriver asked to cancel

Common Values

ValueCountFrequency (%)
Wrong Address 2362
 
1.6%
Change of plans 2353
 
1.6%
Driver is not moving towards pickup location 2335
 
1.6%
Driver asked to cancel 2295
 
1.5%
AC is not working 1155
 
0.8%
(Missing) 139500
93.0%

Length

2025-08-30T14:28:07.284118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T14:28:07.340873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
driver 4630
 
11.0%
not 3490
 
8.3%
is 3490
 
8.3%
wrong 2362
 
5.6%
address 2362
 
5.6%
plans 2353
 
5.6%
of 2353
 
5.6%
change 2353
 
5.6%
moving 2335
 
5.6%
towards 2335
 
5.6%
Other values (7) 13865
33.1%

Most occurring characters

ValueCountFrequency (%)
31428
13.2%
o 20995
 
8.8%
n 18678
 
7.8%
r 17474
 
7.3%
i 16280
 
6.8%
s 15197
 
6.4%
a 13966
 
5.8%
e 13935
 
5.8%
t 10455
 
4.4%
d 9354
 
3.9%
Other values (15) 71104
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 238866
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
31428
13.2%
o 20995
 
8.8%
n 18678
 
7.8%
r 17474
 
7.3%
i 16280
 
6.8%
s 15197
 
6.4%
a 13966
 
5.8%
e 13935
 
5.8%
t 10455
 
4.4%
d 9354
 
3.9%
Other values (15) 71104
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 238866
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
31428
13.2%
o 20995
 
8.8%
n 18678
 
7.8%
r 17474
 
7.3%
i 16280
 
6.8%
s 15197
 
6.4%
a 13966
 
5.8%
e 13935
 
5.8%
t 10455
 
4.4%
d 9354
 
3.9%
Other values (15) 71104
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 238866
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
31428
13.2%
o 20995
 
8.8%
n 18678
 
7.8%
r 17474
 
7.3%
i 16280
 
6.8%
s 15197
 
6.4%
a 13966
 
5.8%
e 13935
 
5.8%
t 10455
 
4.4%
d 9354
 
3.9%
Other values (15) 71104
29.8%

Cancelled Rides by Driver
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing123000
Missing (%)82.0%
Memory size7.9 MiB
1.0
27000 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters81000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 27000
 
18.0%
(Missing) 123000
82.0%

Length

2025-08-30T14:28:07.416029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T14:28:07.448978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 27000
100.0%

Most occurring characters

ValueCountFrequency (%)
1 27000
33.3%
. 27000
33.3%
0 27000
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 81000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 27000
33.3%
. 27000
33.3%
0 27000
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 81000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 27000
33.3%
. 27000
33.3%
0 27000
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 81000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 27000
33.3%
. 27000
33.3%
0 27000
33.3%

Driver Cancellation Reason
Categorical

High correlation  Missing 

Distinct4
Distinct (%)< 0.1%
Missing123000
Missing (%)82.0%
Memory size8.6 MiB
Customer related issue
6837 
The customer was coughing/sick
6751 
Personal & Car related issues
6726 
More than permitted people in there
6686 

Length

Max length35
Median length30
Mean length28.963259
Min length22

Characters and Unicode

Total characters782008
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPersonal & Car related issues
2nd rowCustomer related issue
3rd rowCustomer related issue
4th rowPersonal & Car related issues
5th rowMore than permitted people in there

Common Values

ValueCountFrequency (%)
Customer related issue 6837
 
4.6%
The customer was coughing/sick 6751
 
4.5%
Personal & Car related issues 6726
 
4.5%
More than permitted people in there 6686
 
4.5%
(Missing) 123000
82.0%

Length

2025-08-30T14:28:07.505537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T14:28:07.558996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
customer 13588
 
11.2%
related 13563
 
11.2%
issue 6837
 
5.6%
the 6751
 
5.6%
was 6751
 
5.6%
coughing/sick 6751
 
5.6%
personal 6726
 
5.5%
6726
 
5.5%
car 6726
 
5.5%
issues 6726
 
5.5%
Other values (6) 40116
33.1%

Most occurring characters

ValueCountFrequency (%)
e 114556
14.6%
94261
12.1%
s 67668
 
8.7%
r 60661
 
7.8%
t 53895
 
6.9%
a 40452
 
5.2%
o 40437
 
5.2%
i 40437
 
5.2%
u 33902
 
4.3%
l 26975
 
3.4%
Other values (15) 208764
26.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 782008
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 114556
14.6%
94261
12.1%
s 67668
 
8.7%
r 60661
 
7.8%
t 53895
 
6.9%
a 40452
 
5.2%
o 40437
 
5.2%
i 40437
 
5.2%
u 33902
 
4.3%
l 26975
 
3.4%
Other values (15) 208764
26.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 782008
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 114556
14.6%
94261
12.1%
s 67668
 
8.7%
r 60661
 
7.8%
t 53895
 
6.9%
a 40452
 
5.2%
o 40437
 
5.2%
i 40437
 
5.2%
u 33902
 
4.3%
l 26975
 
3.4%
Other values (15) 208764
26.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 782008
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 114556
14.6%
94261
12.1%
s 67668
 
8.7%
r 60661
 
7.8%
t 53895
 
6.9%
a 40452
 
5.2%
o 40437
 
5.2%
i 40437
 
5.2%
u 33902
 
4.3%
l 26975
 
3.4%
Other values (15) 208764
26.7%

Incomplete Rides
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing141000
Missing (%)94.0%
Memory size8.0 MiB
1.0
9000 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters27000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 9000
 
6.0%
(Missing) 141000
94.0%

Length

2025-08-30T14:28:07.626217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T14:28:07.659488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 9000
100.0%

Most occurring characters

ValueCountFrequency (%)
1 9000
33.3%
. 9000
33.3%
0 9000
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 9000
33.3%
. 9000
33.3%
0 9000
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 9000
33.3%
. 9000
33.3%
0 9000
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 9000
33.3%
. 9000
33.3%
0 9000
33.3%

Incomplete Rides Reason
Categorical

High correlation  Missing 

Distinct3
Distinct (%)< 0.1%
Missing141000
Missing (%)94.0%
Memory size8.1 MiB
Customer Demand
3040 
Vehicle Breakdown
3012 
Other Issue
2948 

Length

Max length17
Median length15
Mean length14.359111
Min length11

Characters and Unicode

Total characters129232
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVehicle Breakdown
2nd rowOther Issue
3rd rowVehicle Breakdown
4th rowOther Issue
5th rowVehicle Breakdown

Common Values

ValueCountFrequency (%)
Customer Demand 3040
 
2.0%
Vehicle Breakdown 3012
 
2.0%
Other Issue 2948
 
2.0%
(Missing) 141000
94.0%

Length

2025-08-30T14:28:07.713877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T14:28:07.762877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
customer 3040
16.9%
demand 3040
16.9%
vehicle 3012
16.7%
breakdown 3012
16.7%
other 2948
16.4%
issue 2948
16.4%

Most occurring characters

ValueCountFrequency (%)
e 21012
16.3%
9000
 
7.0%
r 9000
 
7.0%
s 8936
 
6.9%
m 6080
 
4.7%
o 6052
 
4.7%
a 6052
 
4.7%
d 6052
 
4.7%
n 6052
 
4.7%
u 5988
 
4.6%
Other values (13) 45008
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 129232
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 21012
16.3%
9000
 
7.0%
r 9000
 
7.0%
s 8936
 
6.9%
m 6080
 
4.7%
o 6052
 
4.7%
a 6052
 
4.7%
d 6052
 
4.7%
n 6052
 
4.7%
u 5988
 
4.6%
Other values (13) 45008
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 129232
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 21012
16.3%
9000
 
7.0%
r 9000
 
7.0%
s 8936
 
6.9%
m 6080
 
4.7%
o 6052
 
4.7%
a 6052
 
4.7%
d 6052
 
4.7%
n 6052
 
4.7%
u 5988
 
4.6%
Other values (13) 45008
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 129232
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 21012
16.3%
9000
 
7.0%
r 9000
 
7.0%
s 8936
 
6.9%
m 6080
 
4.7%
o 6052
 
4.7%
a 6052
 
4.7%
d 6052
 
4.7%
n 6052
 
4.7%
u 5988
 
4.6%
Other values (13) 45008
34.8%

Booking Value
Real number (ℝ)

Missing 

Distinct2566
Distinct (%)2.5%
Missing48000
Missing (%)32.0%
Infinite0
Infinite (%)0.0%
Mean508.29591
Minimum50
Maximum4277
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-08-30T14:28:07.830858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile91
Q1234
median414
Q3689
95-th percentile1224
Maximum4277
Range4227
Interquartile range (IQR)455

Descriptive statistics

Standard deviation395.80577
Coefficient of variation (CV)0.77869163
Kurtosis9.8878804
Mean508.29591
Median Absolute Deviation (MAD)211
Skewness2.287351
Sum51846183
Variance156662.21
MonotonicityNot monotonic
2025-08-30T14:28:07.915040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
176 177
 
0.1%
125 174
 
0.1%
200 170
 
0.1%
408 169
 
0.1%
186 168
 
0.1%
353 167
 
0.1%
357 166
 
0.1%
157 166
 
0.1%
470 166
 
0.1%
206 165
 
0.1%
Other values (2556) 100312
66.9%
(Missing) 48000
32.0%
ValueCountFrequency (%)
50 121
0.1%
51 108
0.1%
52 116
0.1%
53 137
0.1%
54 96
0.1%
55 105
0.1%
56 115
0.1%
57 113
0.1%
58 109
0.1%
59 116
0.1%
ValueCountFrequency (%)
4277 1
< 0.1%
4228 1
< 0.1%
4220 1
< 0.1%
4202 1
< 0.1%
4133 1
< 0.1%
4109 1
< 0.1%
4093 1
< 0.1%
4088 1
< 0.1%
4060 1
< 0.1%
4044 1
< 0.1%

Ride Distance
Real number (ℝ)

Missing 

Distinct4901
Distinct (%)4.8%
Missing48000
Missing (%)32.0%
Infinite0
Infinite (%)0.0%
Mean24.637012
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-08-30T14:28:07.999742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.97
Q112.46
median23.72
Q336.82
95-th percentile47.35
Maximum50
Range49
Interquartile range (IQR)24.36

Descriptive statistics

Standard deviation14.002138
Coefficient of variation (CV)0.56833753
Kurtosis-1.2122664
Mean24.637012
Median Absolute Deviation (MAD)12.1
Skewness0.12831234
Sum2512975.2
Variance196.05988
MonotonicityNot monotonic
2025-08-30T14:28:08.245468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.31 43
 
< 0.1%
9.61 43
 
< 0.1%
14.47 42
 
< 0.1%
3.44 41
 
< 0.1%
12.87 41
 
< 0.1%
12.52 40
 
< 0.1%
12.67 39
 
< 0.1%
8.69 39
 
< 0.1%
8.97 39
 
< 0.1%
14.93 38
 
< 0.1%
Other values (4891) 101595
67.7%
(Missing) 48000
32.0%
ValueCountFrequency (%)
1 6
< 0.1%
1.01 3
< 0.1%
1.02 3
< 0.1%
1.03 5
< 0.1%
1.04 3
< 0.1%
1.05 3
< 0.1%
1.06 4
< 0.1%
1.07 6
< 0.1%
1.08 1
 
< 0.1%
1.09 5
< 0.1%
ValueCountFrequency (%)
50 9
 
< 0.1%
49.99 19
< 0.1%
49.98 20
< 0.1%
49.97 22
< 0.1%
49.96 18
< 0.1%
49.95 17
< 0.1%
49.94 21
< 0.1%
49.93 24
< 0.1%
49.92 16
< 0.1%
49.91 18
< 0.1%

Driver Ratings
Real number (ℝ)

High correlation  Missing 

Distinct21
Distinct (%)< 0.1%
Missing57000
Missing (%)38.0%
Infinite0
Infinite (%)0.0%
Mean4.2309925
Minimum3
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-08-30T14:28:08.315466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.3
Q14.1
median4.3
Q34.6
95-th percentile4.9
Maximum5
Range2
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.43687147
Coefficient of variation (CV)0.10325508
Kurtosis0.2802387
Mean4.2309925
Median Absolute Deviation (MAD)0.2
Skewness-0.65571516
Sum393482.3
Variance0.19085668
MonotonicityNot monotonic
2025-08-30T14:28:08.385467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4.3 14081
 
9.4%
4.2 13841
 
9.2%
4.6 9368
 
6.2%
4.4 7018
 
4.7%
4.1 6966
 
4.6%
4.9 4705
 
3.1%
4.7 4678
 
3.1%
4.5 4634
 
3.1%
3.9 3915
 
2.6%
3.8 3848
 
2.6%
Other values (11) 19946
 
13.3%
(Missing) 57000
38.0%
ValueCountFrequency (%)
3 745
 
0.5%
3.1 1459
 
1.0%
3.2 1538
 
1.0%
3.3 1461
 
1.0%
3.4 1491
 
1.0%
3.5 748
 
0.5%
3.6 2026
1.4%
3.7 3790
2.5%
3.8 3848
2.6%
3.9 3915
2.6%
ValueCountFrequency (%)
5 2365
 
1.6%
4.9 4705
 
3.1%
4.8 2328
 
1.6%
4.7 4678
 
3.1%
4.6 9368
6.2%
4.5 4634
 
3.1%
4.4 7018
4.7%
4.3 14081
9.4%
4.2 13841
9.2%
4.1 6966
4.6%

Customer Rating
Real number (ℝ)

High correlation  Missing 

Distinct21
Distinct (%)< 0.1%
Missing57000
Missing (%)38.0%
Infinite0
Infinite (%)0.0%
Mean4.4045839
Minimum3
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-08-30T14:28:08.454468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.6
Q14.2
median4.5
Q34.8
95-th percentile5
Maximum5
Range2
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.43781873
Coefficient of variation (CV)0.099400703
Kurtosis0.64796636
Mean4.4045839
Median Absolute Deviation (MAD)0.3
Skewness-0.88553068
Sum409626.3
Variance0.19168524
MonotonicityNot monotonic
2025-08-30T14:28:08.523467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4.9 11642
 
7.8%
4.6 11533
 
7.7%
4.3 10995
 
7.3%
4.2 10697
 
7.1%
4.5 5890
 
3.9%
4.8 5880
 
3.9%
5 5837
 
3.9%
4.7 5763
 
3.8%
4.1 5396
 
3.6%
4.4 5279
 
3.5%
Other values (11) 14088
 
9.4%
(Missing) 57000
38.0%
ValueCountFrequency (%)
3 468
 
0.3%
3.1 1008
0.7%
3.2 881
 
0.6%
3.3 900
 
0.6%
3.4 928
 
0.6%
3.5 443
 
0.3%
3.6 1194
0.8%
3.7 2354
1.6%
3.8 2357
1.6%
3.9 2370
1.6%
ValueCountFrequency (%)
5 5837
3.9%
4.9 11642
7.8%
4.8 5880
3.9%
4.7 5763
3.8%
4.6 11533
7.7%
4.5 5890
3.9%
4.4 5279
3.5%
4.3 10995
7.3%
4.2 10697
7.1%
4.1 5396
3.6%

Payment Method
Categorical

Missing 

Distinct5
Distinct (%)< 0.1%
Missing48000
Missing (%)32.0%
Memory size7.9 MiB
UPI
45909 
Cash
25367 
Uber Wallet
12276 
Credit Card
10209 
Debit Card
8239 

Length

Max length11
Median length10
Mean length5.5776471
Min length3

Characters and Unicode

Total characters568920
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUPI
2nd rowDebit Card
3rd rowUPI
4th rowUPI
5th rowUPI

Common Values

ValueCountFrequency (%)
UPI 45909
30.6%
Cash 25367
16.9%
Uber Wallet 12276
 
8.2%
Credit Card 10209
 
6.8%
Debit Card 8239
 
5.5%
(Missing) 48000
32.0%

Length

2025-08-30T14:28:08.604369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-30T14:28:08.662953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
upi 45909
34.6%
cash 25367
19.1%
card 18448
13.9%
uber 12276
 
9.2%
wallet 12276
 
9.2%
credit 10209
 
7.7%
debit 8239
 
6.2%

Most occurring characters

ValueCountFrequency (%)
U 58185
10.2%
a 56091
9.9%
C 54024
9.5%
I 45909
 
8.1%
P 45909
 
8.1%
e 43000
 
7.6%
r 40933
 
7.2%
30724
 
5.4%
t 30724
 
5.4%
d 28657
 
5.0%
Other values (7) 134764
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 568920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 58185
10.2%
a 56091
9.9%
C 54024
9.5%
I 45909
 
8.1%
P 45909
 
8.1%
e 43000
 
7.6%
r 40933
 
7.2%
30724
 
5.4%
t 30724
 
5.4%
d 28657
 
5.0%
Other values (7) 134764
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 568920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 58185
10.2%
a 56091
9.9%
C 54024
9.5%
I 45909
 
8.1%
P 45909
 
8.1%
e 43000
 
7.6%
r 40933
 
7.2%
30724
 
5.4%
t 30724
 
5.4%
d 28657
 
5.0%
Other values (7) 134764
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 568920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 58185
10.2%
a 56091
9.9%
C 54024
9.5%
I 45909
 
8.1%
P 45909
 
8.1%
e 43000
 
7.6%
r 40933
 
7.2%
30724
 
5.4%
t 30724
 
5.4%
d 28657
 
5.0%
Other values (7) 134764
23.7%

Interactions

2025-08-30T14:28:02.617728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:00.156575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:00.670146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:01.238087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:01.684433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:02.133526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:02.696503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:00.255932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:00.746655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:01.310673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:01.759141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:02.217005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:02.777099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:00.350664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:00.823796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:01.387310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:01.837398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:02.299424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:02.852080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:00.423176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:00.896888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:01.454259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:01.909340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:02.375597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:02.933371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:00.498688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:01.079571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:01.527290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:01.980501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:02.451884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:03.018716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:00.578688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:01.157813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:01.604290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:02.059093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-30T14:28:02.534677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-30T14:28:08.725617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Avg CTATAvg VTATBooking StatusBooking ValueCustomer RatingDriver Cancellation ReasonDriver RatingsIncomplete Rides ReasonPayment MethodReason for cancelling by CustomerRide DistanceVehicle Type
Avg CTAT1.0000.0590.4710.0010.0000.0000.0000.0000.0000.0000.0990.000
Avg VTAT0.0591.0000.3380.005-0.0030.000-0.0050.0000.0000.0120.0630.000
Booking Status0.4710.3381.0000.0021.0001.0001.0001.0000.0001.0000.3530.002
Booking Value0.0010.0050.0021.000-0.0040.0000.0010.0000.0000.0000.0040.000
Customer Rating0.000-0.0031.000-0.0041.0000.000-0.0020.0000.0000.0000.0050.005
Driver Cancellation Reason0.0000.0001.0000.0000.0001.0000.0000.0000.0000.0000.0000.000
Driver Ratings0.000-0.0051.0000.001-0.0020.0001.0000.0000.0000.000-0.0010.000
Incomplete Rides Reason0.0000.0001.0000.0000.0000.0000.0001.0000.0000.0000.0130.016
Payment Method0.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0050.006
Reason for cancelling by Customer0.0000.0121.0000.0000.0000.0000.0000.0000.0001.0000.0000.168
Ride Distance0.0990.0630.3530.0040.0050.000-0.0010.0130.0050.0001.0000.000
Vehicle Type0.0000.0000.0020.0000.0050.0000.0000.0160.0060.1680.0001.000

Missing values

2025-08-30T14:28:03.184061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-30T14:28:03.449478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-08-30T14:28:03.970461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DateTimeBooking IDBooking StatusCustomer IDVehicle TypePickup LocationDrop LocationAvg VTATAvg CTATCancelled Rides by CustomerReason for cancelling by CustomerCancelled Rides by DriverDriver Cancellation ReasonIncomplete RidesIncomplete Rides ReasonBooking ValueRide DistanceDriver RatingsCustomer RatingPayment Method
02024-03-2312:29:38"CNR5884300"No Driver Found"CID1982111"eBikePalam ViharJhilmilNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
12024-11-2918:01:39"CNR1326809"Incomplete"CID4604802"Go SedanShastri NagarGurgaon Sector 564.914.0NaNNaNNaNNaN1.0Vehicle Breakdown237.05.73NaNNaNUPI
22024-08-2308:56:10"CNR8494506"Completed"CID9202816"AutoKhandsaMalviya Nagar13.425.8NaNNaNNaNNaNNaNNaN627.013.584.94.9Debit Card
32024-10-2117:17:25"CNR8906825"Completed"CID2610914"Premier SedanCentral SecretariatInderlok13.128.5NaNNaNNaNNaNNaNNaN416.034.024.65.0UPI
42024-09-1622:08:00"CNR1950162"Completed"CID9933542"BikeGhitorni VillageKhan Market5.319.6NaNNaNNaNNaNNaNNaN737.048.214.14.3UPI
52024-02-0609:44:56"CNR4096693"Completed"CID4670564"AutoAIIMSNarsinghpur5.118.1NaNNaNNaNNaNNaNNaN316.04.854.14.6UPI
62024-06-1715:45:58"CNR2002539"Completed"CID6800553"Go MiniVaishaliPunjabi Bagh7.120.4NaNNaNNaNNaNNaNNaN640.041.244.04.1UPI
72024-03-1917:37:37"CNR6568000"Completed"CID8610436"AutoMayur ViharCyber Hub12.116.5NaNNaNNaNNaNNaNNaN136.06.564.44.2UPI
82024-09-1412:49:09"CNR4510807"No Driver Found"CID7873618"Go SedanNoida Sector 62Noida Sector 18NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
92024-12-1619:06:48"CNR7721892"Incomplete"CID5214275"AutoRohiniAdarsh Nagar6.126.0NaNNaNNaNNaN1.0Other Issue135.010.36NaNNaNCash
DateTimeBooking IDBooking StatusCustomer IDVehicle TypePickup LocationDrop LocationAvg VTATAvg CTATCancelled Rides by CustomerReason for cancelling by CustomerCancelled Rides by DriverDriver Cancellation ReasonIncomplete RidesIncomplete Rides ReasonBooking ValueRide DistanceDriver RatingsCustomer RatingPayment Method
1499902024-09-2612:31:22"CNR3212810"Cancelled by Driver"CID6199171"AutoKashmere Gate ISBTGTB Nagar10.7NaNNaNNaN1.0Personal & Car related issuesNaNNaNNaNNaNNaNNaNNaN
1499912024-07-1314:47:30"CNR5591053"Completed"CID1829616"AutoKarol BaghVishwavidyalaya11.230.8NaNNaNNaNNaNNaNNaN597.027.914.24.3UPI
1499922024-01-2418:02:28"CNR1717894"No Driver Found"CID9564749"Go MiniNehru PlaceAya NagarNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1499932024-05-0311:18:17"CNR9715958"Completed"CID8835432"Go MiniTughlakabadJama Masjid13.220.1NaNNaNNaNNaNNaNNaN280.029.894.14.7Cash
1499942024-06-1416:46:53"CNR9572383"Completed"CID2952237"Go MiniAkshardhamGreater Noida8.230.6NaNNaNNaNNaNNaNNaN388.010.514.34.9UPI
1499952024-11-1119:34:01"CNR6500631"Completed"CID4337371"Go MiniMG RoadGhitorni10.244.4NaNNaNNaNNaNNaNNaN475.040.083.74.1Uber Wallet
1499962024-11-2415:55:09"CNR2468611"Completed"CID2325623"Go MiniGolf Course RoadAkshardham5.130.8NaNNaNNaNNaNNaNNaN1093.021.314.85.0UPI
1499972024-09-1810:55:15"CNR6358306"Completed"CID9925486"Go SedanSatguru Ram Singh MargJor Bagh2.723.4NaNNaNNaNNaNNaNNaN852.015.933.94.4Cash
1499982024-10-0507:53:34"CNR3030099"Completed"CID9415487"AutoGhaziabadSaidulajab6.939.6NaNNaNNaNNaNNaNNaN333.045.544.13.7UPI
1499992024-03-1015:38:03"CNR3447390"Completed"CID4108667"Premier SedanAshok Park MainGurgaon Sector 293.533.7NaNNaNNaNNaNNaNNaN806.021.194.64.9Credit Card